Learning Network Topology from Simple Sensor Data

Authors:
Dimitri Marinakis;Philippe Giguère;Gregory Dudek
Affiliations:
Centre for Intelligent Machines, McGill University, 3480 University St, Montreal, Quebec, H3A 2A7, Canada;Centre for Intelligent Machines, McGill University, 3480 University St, Montreal, Quebec, H3A 2A7, Canada;Centre for Intelligent Machines, McGill University, 3480 University St, Montreal, Quebec, H3A 2A7, Canada
Venue:
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Year:
2007

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Abstract

In this paper, we present an approach for recovering a topological map of the environment using only detection events from a deployed sensor network. Unlike other solutions to this problem, our technique operates on timestamp freeobservational data; i.e.no timing information is exploited by our algorithm except the ordering. We first give a theoretical analysis of this version of the problem, and then we show that by considering a sliding window over the observations, the problem can be re-formulated as a version of set-covering. We present two heuristics based on this set-covering formulation and evaluate them with numerical simulations. The experiments demonstrate that promising results can be obtained using a greedy algorithm.